Insight into glycogen synthase kinase-3β inhibitory activity of phyto-constituents from Melissa officinalis: in silico studies

Over activity of Glycogen synthase kinase-3β (GSK-3β), a serine/threonine-protein kinase has been implicated in a number of diseases including stroke, type II diabetes and Alzheimer disease (AD). This study aimed to find novel inhibitors of GSK-3β from phyto-constituents of Melissa officinalis with the aid of computational analysis. Molecular docking, induced-fit docking (IFD), calculation of binding free energy via the MM-GBSA approach and Lipinski's rule of five (RO5) were employed to filter the compounds and determine their druggability. Most importantly, the compounds pIC₅₀ were predicted by machine learning-based model generated by AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best model obtained was Model kpls_desc_38 (R² = 0.8467 and Q² = 0.8069), and this external validated model was utilized to predict the bioactivities of the lead compounds. While a number of characterized compounds from Melissa officinalis showed better docking score, binding free energy alongside adherence to RO5 than co-cystallized ligand, only three compounds (salvianolic acid C, ellagic acid and naringenin) showed more satisfactory pIC₅₀. The results obtained in this study can be useful to design potent inhibitors of GSK-3β.

Keywords: AutoQSAR; Glycogen synthase kinase-3β; Induced-fit docking (IFD); MM-GBSA; Melissa officinalis.